This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by the Frontiers Research Foundation. The published article can be found at: http://www.frontiersin.org/Microbiology.

Descriptions

Complex symbioses between animal or plant hosts and their associated microbiotas can
involve thousands of species and millions of genes. Because of the number of interacting
partners, it is often impractical to study all organisms or genes in these host-microbe
symbioses individually. Yet new phylogenetic predictive methods can use the wealth of
accumulated data on diverse model organisms to make inferences into the properties
of less well-studied species and gene families. Predictive functional profiling methods
use evolutionary models based on the properties of studied relatives to put bounds
on the likely characteristics of an organism or gene that has not yet been studied in
detail. These techniques have been applied to predict diverse features of host-associated
microbial communities ranging from the enzymatic function of uncharacterized genes to the
gene content of uncultured microorganisms. We consider these phylogenetically informed
predictive techniques from disparate fields as examples of a general class of algorithms for
Hidden State Prediction (HSP), and argue that HSP methods have broad value in predicting
organismal traits in a variety of contexts, including the study of complex host-microbe
symbioses.